With the growing interest in social applications of Natural Language Processing and Computational Argumentation, a natural question is how controversial a given concept is. Prior works relied on Wikipedia's metadata and on content analysis of the articles pertaining to a concept in question. Here we show that the immediate textual context of a concept is strongly indicative of this property, and, using simple and language-independent machine-learning tools, we leverage this observation to achieve state-of-the-art results in controversiality prediction. In addition, we analyze and make available a new dataset of concepts labeled for controversiality. It is significantly larger than existing datasets, and grades concepts on a 0-10 scale, rather than treating controversiality as a binary label.
@article{arxiv.1908.07491,
title = {Controversy in Context},
author = {Benjamin Sznajder and Ariel Gera and Yonatan Bilu and Dafna Sheinwald and Ella Rabinovich and Ranit Aharonov and David Konopnicki and Noam Slonim},
journal= {arXiv preprint arXiv:1908.07491},
year = {2019}
}